X-ray CT system and medical processing apparatus

ABSTRACT

An X-ray CT system according to an embodiment includes processing circuitry configured to: execute first scanning of acquiring a first subject data set corresponding to first X-ray energy by irradiating a first region of a subject with X-rays; execute, after the first scanning, second scanning of acquiring a second subject data set corresponding to second X-ray energy and a third subject data set corresponding to third X-ray energy different from the second X-ray energy by irradiating a second region included in the first region with X-rays; and perform material decomposition among a plurality of reference materials based on: a fourth subject data set obtained based on the first subject data set and one of the second subject data set and the third subject data set; and the other of the second subject data set and the third subject data set.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is based upon and claims the benefit of priority fromJapanese Patent Application No. 2019-126685, filed on Jul. 8, 2019; andJapanese Patent Application No. 2020-107894, filed on Jun. 23, 2020, theentire contents of all of which are incorporated herein by reference.

FIELD

Embodiments described herein relate generally to an X-ray CT system anda medical processing apparatus.

BACKGROUND

According to an existing technology, material decomposition of an objectis performed by using a plurality of reference materials based onprojection data corresponding to two or more kinds of X-ray energy andacquired by an X-ray computed tomography (CT) scanner, and a result ofthe material decomposition is displayed as an image. When two kinds ofX-ray energy are used, this technology is called dual energy (DE), andmaterial decomposition between two kinds of reference materials ispossible.

For example, with the dual energy technology, it is possible todecompose materials such as a kidney stone, fat, soft tissue, and a bonein the body of a subject. In addition, with the dual energy technology,it is possible to determine whether a kidney stone in the body of thesubject is a calcium stone or a uric acid stone.

In some cases, noise and artifacts are included in projection dataacquired by an X-ray CT scanner due to various kinds of factors. Inparticular, when the physical size of a subject is large, an X-ray islikely to attenuate, and decrease of the quality of the projection datais significant. When high-energy and high-radiation-dose X-rays areused, the quality of the projection data improves but the amount ofradiation exposure of the subject increases. When the energy of X-rayson the low-energy side in dual energy is increased, the accuracy ofmaterial decomposition potentially decreases due to the energydifference decrease.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram illustrating an exemplary configuration of anX-ray CT system according to a first embodiment;

FIG. 2 is a diagram illustrating exemplary processing at the X-ray CTsystem according to the first embodiment;

FIG. 3 is a diagram for description of projection data set correctionprocessing according to the first embodiment;

FIG. 4A is a diagram for description of segmentation according to thefirst embodiment;

FIG. 4B is a diagram for description of the segmentation according tothe first embodiment;

FIG. 4C is a diagram for description of the segmentation according tothe first embodiment;

FIG. 5 is a flowchart for description of a series of processes ofprocessing at the X-ray CT system according to the first embodiment; and

FIG. 6 is a block diagram illustrating an exemplary configuration of amedical information processing system according to a second embodiment.

DETAILED DESCRIPTION

An X-ray CT system includes processing circuitry. The processingcircuitry executes first scanning of acquiring a first subject data setcorresponding to first X-ray energy by irradiating a first region of asubject with X-rays and executes, after the first scanning, secondscanning of acquiring a second subject data set corresponding to secondX-ray energy and a third subject data set corresponding to third X-rayenergy different from the second X-ray energy by irradiating a secondregion included in the first region with X-rays. In addition, theprocessing circuitry performs material decomposition among a pluralityof reference materials based on: a fourth subject data set obtainedbased on the first subject data set and one of the second and thirdsubject data sets; and the other of the second and third subject datasets.

Embodiments of an X-ray CT system and a medical processing apparatuswill be described below in detail with the accompanying drawings. AnX-ray CT system and a medical processing apparatus according to thepresent application are not limited by the embodiments described below.

The following first describes the configuration of an X-ray CT system 10according to a first embodiment with reference to FIG. 1. FIG. 1 is ablock diagram illustrating an exemplary configuration of the X-ray CTsystem 10 according to the first embodiment. As illustrated in FIG. 1,the X-ray CT system 10 includes a gantry 110, a bed 130, and a console140. The X-ray CT system 10 is also called an X-ray CT apparatus or anX-ray CT scanner.

In FIG. 1, a Z-axis direction is defined to be along the rotational axisof a rotation frame 113 not being tilted or the longitudinal directionof a couchtop 133 of the bed 130. An X-axis direction is defined to beorthogonal to the Z-axis direction and horizontal to the floor surface.A Y-axis direction is defined to be orthogonal to the Z-axis directionand vertical to the floor surface. FIG. 1 illustrates the gantry 110 ina plurality of directions for description and corresponds to a case inwhich the X-ray CT system 10 includes one gantry 110.

The gantry 110 includes an X-ray tube 111, an X-ray detector 112, therotation frame 113, an X-ray high-voltage device 114, a control device115, a wedge 116, a collimator 117, and a DAS 118.

The X-ray tube 111 is a vacuum tube including a cathode (filament)generates thermions, and an anode (target) that generates X-rays uponcollision with thermions. The X-ray tube 111 receives high-voltageapplication from the X-ray high-voltage device 114 and emits thermionsfrom the cathode toward the anode, thereby generating X-rays with whicha subject P1 is to be irradiated.

The X-ray detector 112 detects X-rays emitted from the X-ray tube 111and having passed through the subject P1 and outputs a signalcorresponding to the amount of detected X-rays to the DAS 118. The X-raydetector 112 includes, for example, a plurality of detection elementcolumns each including a plurality of detection elements arrayed in achannel direction along one arc centered at a focal point of the X-raytube 111. The X-ray detector 112 has a structure in which, for example,a plurality of detection element columns each including a plurality ofdetection elements arrayed in the channel direction are arrayed in acolumn direction (slice direction or row direction).

For example, the X-ray detector 112 is an indirect conversion detectorincluding a grid, a scintillator array, and a light sensor array. Thescintillator array includes a plurality of scintillators. Eachscintillator includes a scintillator crystal that outputs light in theamount of photons in accordance with the amount of incident X-rays. Thegrid is disposed on a surface of the scintillator array on the X-rayincident side and includes an X-ray shielding plate that absorbsscattering X-rays. The grid is also called a collimator (one-dimensionalcollimator or two-dimensional collimator). The light sensor array has afunction to perform conversion into an electric signal in accordancewith the quantity of light from each scintillator and includes a lightsensor such as a photodiode. The X-ray detector 112 may be a directconversion detector including a semiconductor element configured toconvert an incident X-ray into an electric signal.

The rotation frame 113 is an annular frame that oppositely supports theX-ray tube 111 and the X-ray detector 112 and rotates the X-ray tube 111and the X-ray detector 112 through the control device 115. For example,the rotation frame 113 is a cast metal made of aluminum. The rotationframe 113 may further support the X-ray high-voltage device 114, thewedge 116, the collimator 117, the DAS 118, and the like in addition tothe X-ray tube 111 and the X-ray detector 112. In addition, the rotationframe 113 may further support various kinds of components (notillustrated) in FIG. 1. Hereinafter, the rotation frame 113 and any partthat rotates together with the rotation frame 113 in the gantry 110 arealso referred to as a rotation unit.

The X-ray high-voltage device 114 includes a high-voltage generationdevice including electric circuitry such as a transformer and arectifier and configured to generate high voltage to be applied to theX-ray tube 111, and an X-ray control device configured to control outputvoltage in accordance with X-rays generated by the X-ray tube 111. Thehigh-voltage generation device may be of a transformer scheme or aninverter scheme. The X-ray high-voltage device 114 may be provided tothe rotation frame 113 or a fixation frame (not illustrated).

The control device 115 includes processing circuitry including a centralprocessing unit (CPU) and the like, and a drive mechanism such as amotor or an actuator. The control device 115 receives an input signalfrom an input interface 143 and controls operation of the gantry 110 andthe bed 130. For example, the control device 115 controls rotation ofthe rotation frame 113, tilt of the gantry 110, and operation of the bed130. For example, as control to tilt the gantry 110, the control device115 rotates the rotation frame 113 about an axis parallel to the X-axisdirection based on input tilt angle information. The control device 115may be provided to the gantry 110 or the console 140.

The wedge 116 is an X-ray filter for adjusting the amount of X-raysemitted from the X-ray tube 111. Specifically, the wedge 116 is an X-rayfilter that attenuates X-rays emitted from the X-ray tube 111 to thesubject P1 so that the X-rays have predetermined distribution. Forexample, the wedge 116 is a wedge filter or a bow-tie filter andproduced by fabricating aluminum or the like to have a predeterminedtarget angle and a predetermined thickness.

The collimator 117 is a lead plate or the like for narrowing theirradiation range of X-rays having transmitted through the wedge 116 andis formed as a slit by combining a plurality of lead plates or the like.The collimator 117 is also called an X-ray aperture. In FIG. 1, thewedge 116 is disposed between the X-ray tube 111 and the collimator 117,but the collimator 117 may be disposed between the X-ray tube 111 andthe wedge 116. In this case, the wedge 116 transmits and attenuatesX-rays emitted from the X-ray tube 111 and having an irradiation rangerestricted by the collimator 117.

The DAS 118 acquires a signal of an X-ray detected by each detectionelement included in the X-ray detector 112. For example, the DAS 118includes an amplifier configured to perform amplification processing onan electric signal output from each detection element, and an A/Dconverter configured to convert the electric signal into a digitalsignal, and generates detection data. The DAS 118 is achieved by, forexample, a processor.

The data generated by the DAS 118 is transmitted from a transmitterincluding a light-emitting diode (LED) and provided to the rotationframe 113 to a receiver including a photodiode and provided to anon-rotating part (for example, the fixation frame; omitted in FIG. 1)of the gantry 110, through optical communication, and is forwarded tothe console 140. The non-rotating part is, for example, the fixationframe rotatably supporting the rotation frame 113. The method of datatransmission from the rotation frame 113 to the non-rotating part of thegantry 110 is not limited to optical communication but may employ anynon-contact data transmission scheme or any contact data transmissionscheme.

The bed 130 is an apparatus on which the subject P1 as a scanning targetis placed and moved, and includes a base 131, a couch drive device 132,the couchtop 133, and a support frame 134. The base 131 is a housingmovably supporting the support frame 134 in the vertical direction. Thecouch drive device 132 is a drive mechanism configured to move, in thelong axis direction of the couchtop 133, the couchtop 133 on which thesubject P1 is placed, and includes a motor, an actuator, and the like.The couchtop 133 provided on the upper surface of the support frame 134is a plate on which the subject P1 is placed. The couch drive device 132may move, in addition to the couchtop 133, the support frame 134 in thelong axis direction of the couchtop 133.

The console 140 includes a memory 141, a display 142, the inputinterface 143, and processing circuitry 144. In the followingdescription, the console 140 is separated from the gantry 110, but theconsole 140 or some components of the console 140 may be included in thegantry 110.

The memory 141 is achieved by a semiconductor memory element such as arandom access memory (RAM) or a flash memory, a hard disk, an opticaldisk, or the like. For example, the memory 141 stores various kinds ofdata acquired through execution of scanning on the subject P1. Inaddition, for example, the memory 141 stores a computer program forcircuitry included in the X-ray CT system 10 to achieve its function.The memory 141 may be achieved by servers (cloud) connected with theX-ray CT system 10 through a network.

The display 142 displays various kinds of information. For example, thedisplay 142 displays a display CT image generated by the processingcircuitry 144 and displays an image indicating a result of materialdecomposition. For example, the display 142 also displays a graphicaluser interface (GUI) for receiving various operations from a user. Forexample, the display 142 is a liquid crystal display or a cathode raytube (CRT) display. The display 142 may be a desktop display, a tabletterminal capable of performing wireless communication with the console140, or the like.

The input interface 143 receives various input operations from the user,converts such a received input operation into an electric signal, andoutputs the electric signal to the processing circuitry 144. Forexample, the input interface 143 receives a reconstruction condition onreconstruction of CT image data, an image processing condition ongeneration of a display CT image from CT image data, and the like fromthe user. For example, the input interface 143 is achieved by a mouse, akeyboard, a truck ball, a switch, a button, a joystick, a touch pad onwhich an input operation is performed through touch on an operationsurface, a touch screen as integration of a display screen and a touchpad, non-contact input circuitry using an optical sensor, voice inputcircuitry, or the like. The input interface 143 may be provided to thegantry 110. The input interface 143 may be achieved by a tablet terminalor the like capable of performing wireless communication with theconsole 140. The input interface 143 is not limited to a configurationincluding a physical operation component such as a mouse or a keyboard.Examples of the input interface 143 include electric signal processingcircuitry that receives an electric signal corresponding to an inputoperation from an external input instrument provided separately from theconsole 140 and outputs the electric signal to the processing circuitry144.

The processing circuitry 144 controls operation of the entire X-ray CTsystem 10 by executing a scanning function 144 a, a processing function144 b, and a control function 144 c. The scanning function 144 a is anexemplary scanning unit. The processing function 144 b is an exemplaryprocessing unit.

For example, the processing circuitry 144 executes scanning on thesubject P1 by reading a computer program corresponding to the scanningfunction 144 a from the memory 141 and executing the computer program.For example, the scanning function 144 a supplies high voltage to theX-ray tube 111 by controlling the X-ray high-voltage device 114.Accordingly, the X-ray tube 111 generates X-rays to be emitted to thesubject P1. The scanning function 144 a moves the subject P1 into animage capturing port of the gantry 110 by controlling the couch drivedevice 132. The scanning function 144 a controls distribution of X-raysto be emitted to the subject P1 by adjusting the position of the wedge116 and the opening degree and position of the collimator 117. Thescanning function 144 a rotates the rotation unit by controlling thecontrol device 115. While scanning is executed by the scanning function144 a, the DAS 118 acquires an X-ray signal from each detection elementof the X-ray detector 112 and generates detection data. The scanningfunction 144 a provides preprocessing to the detection data output fromthe DAS 118. For example, the scanning function 144 a providespreprocessing such as logarithmic conversion processing, offsetcorrection processing, inter-channel sensitivity correction processing,or beam hardening correction to the detection data output from the DAS118. The data provided with the preprocessing is also referred to as rawdata. The detection data yet to be provided with the preprocessing andthe raw data provided with the preprocessing are also collectivelyreferred to as projection data.

The processing circuitry 144 generates image data based on theprojection data provided with the preprocessing by reading a computerprogram corresponding to the processing function 144 b from the memory141 and executing the computer program. For example, the processingfunction 144 b generates CT image data (volume data) by performing, onthe projection data, reconstruction processing using a filter correctionback projection method, an iterative approximation reconstructionmethod, an adaptive iterative approximation reconstruction method, orthe like. Alternatively, the processing function 144 b may generate CTimage data by performing reconstruction processing by artificialintelligence (AI). For example, the processing function 144 b generatesCT image data by a deep learning reconstruction (DLR) method. Inaddition, the processing function 144 b performs material decompositionamong a plurality of reference materials based on the projection data.The processing function 144 b may perform material decomposition basedon data yet to be provided with the reconstruction processing(projection data) or based on data provided with the reconstructionprocessing (CT image data). Decomposition processing by the processingfunction 144 b will be described later.

The processing circuitry 144 controls display on the display 142 byreading a computer program corresponding to the control function 144 cfrom the memory 141 and executing the computer program. For example, thecontrol function 144 c converts the CT image data generated by theprocessing function 144 b into a display CT image (such ascross-sectional image data of an optional section or three-dimensionalimage data) by a well-known method based on an input operation receivedfrom the user through the input interface 143 or the like. Then, thecontrol function 144 c causes the display 142 to display the converteddisplay CT image. In addition, for example, the control function 144 ccauses the display 142 to display an image indicating a result ofmaterial decomposition by the processing function 144 b. The controlfunction 144 c transmits various kinds of data through a network. Forexample, the control function 144 c transmits the CT image datagenerated by the processing function 144 b and the image indicating aresult of material decomposition to an image storage (not illustrated)and stores the CT image data and the image in the image storage.

In the X-ray CT system 10 illustrated in FIG. 1, each processingfunction is stored in the memory 141 in the form of acomputer-executable program. The processing circuitry 144 is a processorconfigured to read a computer program from the memory 141 and executethe computer program to achieve a function corresponding to the computerprogram. In other words, the processing circuitry 144 having read acomputer program has a function corresponding to the read computerprogram.

The scanning function 144 a, the processing function 144 b, and thecontrol function 144 c are achieved by the single processing circuitry144 in the above description with reference to FIG. 1, but theprocessing circuitry 144 may be configured as a combination of aplurality of independent processors, and each processor may execute acomputer program to achieve the corresponding function. The processingfunctions of the processing circuitry 144 may be distributed orintegrated to one or a plurality of processing circuits as appropriate.

Alternatively, the processing circuitry 144 may achieve a function byusing a processor of an external apparatus connected through a network.For example, the processing circuitry 144 achieves each functionillustrated in FIG. 1 by reading and executing the computer programcorresponding to the function from the memory 141 and by using, ascalculation resources, servers (cloud) connected with the X-ray CTsystem 10 through a network.

The exemplary configuration of the X-ray CT system 10 is describedabove. The following describes processing performed by the X-ray CTsystem 10 in detail.

The processing performed by the X-ray CT system 10 will be firstdescribed below with reference to FIG. 2. FIG. 2 illustrates a case inwhich Scanning A12 of a kV switching scheme is executed and materialdecomposition between two kinds of reference materials is performed.FIG. 2 is a diagram illustrating exemplary processing at the X-ray CTsystem 10 according to the first embodiment.

As illustrated in FIG. 2, the scanning function 144 a first executesScanning A11. Specifically, the scanning function 144 a acquiresprojection data for each of a plurality of irradiation directions(views) by irradiating a range R1 extending in the body axis directionof the subject P1 with X-rays of Energy E11 while rotating the focalposition of the X-rays around the subject P1 as illustrated in FIG. 2.Hereinafter, a plurality of pieces of projection data is also referredto as a projection data set.

Accordingly, the scanning function 144 a acquires Projection data setB11 corresponding to Energy E11 by irradiating the range R1 with X-rays.For example, the scanning function 144 a acquires Projection data setB11 by executing conventional scanning, helical scanning, step-and-shootscanning, or the like. The processing function 144 b reconstructsthree-dimensional Image data C11 based on Projection data set B11acquired by Scanning A11.

Scanning A11 is exemplary first scanning. The range R1 is an exemplaryfirst range or first region. Energy E11 is an exemplary first X-rayenergy. Projection data set B11 is an exemplary first projection dataset or first subject data set. Image data C11 is an exemplary firstimage data.

Subsequently, the control function 144 c generates a reference image byexecuting rendering processing on Image data C11 and causes thegenerated reference image to display the display 142. An example of therendering processing is processing of generating a two-dimensional imageof an optional section from Image data C11 by multi planarreconstruction (MPR). Another exemplary of the rendering processing isprocessing of generating a two-dimensional image on whichthree-dimensional information is reflected from Image data C11 by volumerendering processing or maximum intensity projection (MIP). In addition,the control function 144 c sets a range R2 as the scanning range ofScanning A12 by receiving an input operation from a user referring tothe reference image. Scanning A12 is exemplary second scanning. Therange R2 is an exemplary second range or second region.

In other words, Scanning A11 is positioning image capturing for settingthe range R2, and Image data C11 is positioning image data used to setthe range R2. Thus, the range R1 of Scanning A11 is preferably set to bea relatively wide range including an organ or the like as a diagnosistarget. The range R2 is set in the range R1 and thus typically smallerthan the range R1 as illustrated in FIG. 2. In other words, the range R2is included in the range R1. The positioning image data is also calledscano-image data, scanogram, or scout image data. Image data C11 asthree-dimensional scanogram is also referred to as 3D scanogram.

Subsequently, the scanning function 144 a executes Scanning Alt in therange R2 set to the reference image. Specifically, the scanning function144 a executes Scanning A12 by changing the energy of X-rays emittedfrom the X-ray tube 111 to the subject P1 between Energy E12 and EnergyE13 for each of one or a plurality of views. Accordingly, the scanningfunction 144 a acquires Projection data set B12 corresponding to EnergyE12 and Projection data set B13 corresponding to Energy E13.

Energy E12 is exemplary second X-ray energy. Projection data set B12 isan exemplary second projection data set or second subject data set.Energy E13 is exemplary third X-ray energy. Projection data set B13 isan exemplary third projection data set or third subject data set. EnergyE12 is lower than Energy E13. Energy E11 may be different from EnergyE12 and Energy E13 or equal to any of Energy E12 and Energy E13.

Subsequently, the processing function 144 b generates Projection dataset B14 corresponding to Energy E12 based on Projection data set B11. Inaddition, the processing function 144 b generates Projection data setB16 corresponding to Energy E12 by correcting Projection data set B12based on Projection data set B14. Accordingly, the processing function144 b generates Projection data set B16 based on Projection data set B12and Projection data set B14. Projection data set B14 is an exemplaryfourth projection data set, and Projection data set B16 is an exemplarysixth projection data set or fourth subject data set. The generationprocessing of Projection data set B14 and Projection data set B16 willbe described later.

Then, the processing function 144 b executes material decompositionbetween two kinds of reference materials based on Projection data setB16 corresponding to Energy E11 and Projection data set B13corresponding to Energy E13 and generates a material decompositionimage.

For example, the processing function 144 b separates two referencematerials in the range R2 by using Projection data set B16 andProjection data set B13. Specifically, the processing function 144 bdetermines distribution of the linear attenuation coefficient for eachof Projection data set B16 and Projection data set B13 and solves asystem of equations in Expression (1) below for each position (pixel) ofthe linear attenuation coefficient, thereby calculating the mixtureamount or mixture ratio of the two reference materials at the position.

$\begin{matrix} \begin{matrix}{{µ( {E\; 1} )} = {{{µ_{\alpha}( {E1} )}c_{\alpha}} + {{µ_{\beta}( {E1} )}c_{\beta}}}} \\{{µ( {E\; 2} )} = {{{µ_{\alpha}( {E\; 2} )}c_{\alpha}} + {{µ_{\beta}( {E2} )}c_{\beta}}}}\end{matrix} \} & (1)\end{matrix}$

In the expression, “μ(E1)” represents the linear attenuation coefficientof each position at single color X-ray energy “E1”, and “μ(E2)”represents the linear attenuation coefficient of each position at singlecolor X-ray energy “E2”. In addition, “μα(E)” represents the linearattenuation coefficient of Reference material α, and “μβ(E)” representsthe linear attenuation coefficient of Reference material β. In addition,“cα” represents the mixture amount of Reference material α, and “cβ”represents the mixture amount of Reference material β. The linearattenuation coefficient for the energy of each reference material isknown. For example, the processing function 144 b substitutes Energy E11into “E1” and Energy E12 into “E2” and solves the system of equations ofExpression (1), thereby performing material decomposition between thetwo kinds of reference materials “α and β”.

Then, the processing function 144 b generates an image illustrating aresult of the material decomposition. For example, the processingfunction 144 b generates a material decomposition image for eachreference material. For example, the processing function 144 b generatesa material decomposition image in which Reference material α illustratesin an emphasized manner, and a material decomposition image in whichReference material β is illustrated in an emphasized manner. Inaddition, the processing function 144 b may generate various kinds ofimages at predetermined energy such as a virtual single-color X-rayimage (also referred to as a monochromatic image), a density image, andan effective atomic number image by performing weighted calculationprocessing based on the mixture ratio of each reference material byusing a plurality of material decomposition images generated for therespective reference materials. The control function 144 c causes thedisplay 142 to display an image illustrating these materialdecomposition results.

In the above description, material decomposition is performed before thereconstruction processing is provided, but the processing function 144 bmay perform material decomposition after the reconstruction processingis provided. Specifically, the processing function 144 b may performmaterial decomposition by solving the system of equations of Expression(1) for each pixel of Projection data set B16 and Projection data setB13 or by solving the system of equations of Expression (1) for eachpixel of a CT image data based on Projection data set B16 and a CT imagedata based on Projection data set B13.

In Expression (1) described above, “μ” represents the linear attenuationcoefficient, and “c” represents the mixture amount, but the embodimentis not limited thereto. For example, the processing function 144 b maysolve Expression (1) with “μ” as the mass attenuation coefficient and“c” as the density for each material.

The following describes the generation processing of Projection data setB14 and Projection data set B16 in more detail with reference to FIG. 3.Specifically, as illustrated in FIG. 3, the X-ray CT system 10 generatesProjection data set B14 from Projection data set B11 acquired byScanning A11, and generates Projection data set B16 by correcting, byusing Projection data set B14, Projection data set B12 acquired byScanning A12. Thus, FIG. 3 is a diagram for description of thecorrection processing of Projection data set B12 according to the firstembodiment.

More specifically, the scanning function 144 a first executes ScanningA11 and acquires Projection data set B11 corresponding to Energy E11.Projection data set B11 can be expressed as data having axes in achannel direction and a view direction as illustrated in FIG. 3.Scanning A11 is executed by using X-rays of single energy (Energy E11),and thus Projection data set B11 is data of Energy E11 for any view.Thus, Scanning A11 is single-energy scanning using X-rays of Energy E11.

Although not illustrated in FIG. 3, Projection data set B11 isthree-dimensional data having the body axis direction of the subject P1(the Z-axis direction). For example, Projection data set B11 has matrixsizes of “P” in the channel direction, “Q” in the view direction, and“R” in the body axis direction.

Subsequently, the processing function 144 b generates three-dimensionalImage data C11 by executing the reconstruction processing based onProjection data set B11. Image data C11 is three-dimensional dataindicating distribution of a CT value (unit: HU).

Subsequently, the processing function 144 b segments Image data C11 inaccordance with the CT value. Specifically, the processing function 144b performs tissue classification of each pixel in Image data C11 intoair, water, soft tissue, bone, and the like in accordance with the CTvalue. The CT value is proportional to an X-ray absorption coefficient.Thus, the processing function 144 b segments Image data C11 inaccordance with the X-ray absorption coefficient.

The following describes the segmentation of Image data C11 by theprocessing function 144 b in more detail with reference to FIGS. 4A, 4B,and 4C. FIGS. 4A, 4B, and 4C are diagrams for description of thesegmentation according to the first embodiment.

FIG. 4A illustrates the segmented Image data C11. For example, theprocessing function 144 b classifies each pixel in Image data C11 intoany of tissues such as air, water, soft tissue, and bone. Differenttissues indicate different attenuation coefficients, respectively. Forexample, as illustrated in FIG. 4B, the attenuation coefficient isdifferent between a bone and a soft tissue for irradiation with X-raysof the same Energy E11. Thus, “μbone(E11)≠μsoft tissue(E11)” holds.Different X-ray energies indicate different attenuation coefficients,respectively. For example, as illustrated in FIG. 4C, the attenuationcoefficient is different between X-rays of Energy E11 and X-rays ofEnergy E11 for irradiation of an identical tissue “bone” with X-rays.Thus, “μbone(E11) # μbone(E12)” holds. In the above description, thesegmentation is performed for each pixel, but the processing function144 b may perform segmentation for each pixel group as a bundle of aplurality of pixels.

Subsequently, the processing function 144 b generates Projection dataset B14 by sequentially projecting Image data C11 in accordance withEnergy E12. Specifically, the attenuation coefficient for the energy ofeach tissue is known, and thus the processing function 144 b cansimulate a projection data set acquired at Energy E11 by sequentiallyprojecting, in accordance with Energy E12, Image data C11 segmented foreach tissue and can generate Projection data set B14. Accordingly,Projection data set B11 is a projection data set actually acquired byScanning A11, whereas Projection data set B14 is a simulated projectiondata set. For example, the processing function 144 b generatesProjection data set B14 by calculating attenuation of an X-ray of EnergyE12 that occurs when the X-ray transmits through various tissues.

Subsequently, the processing function 144 b performs resampling ofProjection data set B14. Specifically, Projection data set B14 generatedthrough sequentially projection has a “P×Q×R” matrix that is same asthat of Projection data set B11, but the matrix of Projection data setB14 is different from the matrix of the projection data set acquired byScanning A12 in some cases. Thus, the processing function 144 b performsresampling of Projection data set B14 so that the dimensions of thematrix of Projection data set B14 become equal to the dimensions of thematrix of the projection data set acquired by Scanning A12.

For example, the projection data set acquired by Scanning A12 has a“P×M×N” matrix. Specifically, the matrix size in the channel directiontypically does not change, and thus the projection data set acquired byScanning A12 has the matrix size “P” in the channel direction likeProjection data set B14. However, the matrix sizes in the view directionand the body axis direction can change for each scanning. For example,Projection data set B14 has the matrix size “Q” in the view directionand the matrix size “R” in the body axis direction, whereas theprojection data set acquired by Scanning A12 has the matrix size “M” inthe view direction and the matrix size “N” in the body axis direction.Thus, the processing function 144 b performs resampling so that thematrix of Projection data set B14 has a size of “P×M×N”.

Subsequently, the processing function 144 b fabricates Projection dataset B14 into a sparse state. Specifically, the processing function 144 bgenerates sparse Projection data set B14 by fabricating Projection dataset B14 generated through sequential projection into a sparse statesimilar to that of Projection data set B12 corresponding to Energy E12.

Specifically, the projection data set acquired by Scanning A12 is aprojection data set in which a plurality of energies (Energy E12 andEnergy E13) are mixed as illustrated in FIG. 3. Projection data set B12separated from the projection data set acquired by Scanning A12 issparse data in which data corresponding to the view of Energy E13 ismissing. Thus, the processing function 144 b fabricates Projection dataset B14 into a sparse state similar to that of Projection data set B12.

The processing process illustrated in FIG. 3 is merely exemplary and maybe changed as appropriate. For example, the processing function 144 bmay first fabricate Projection data set B14 into a sparse state and thenperform resampling from “P×Q×R” to “P×M×N”. For example, the processingfunction 144 b may perform resampling of Projection data set B11 andthen reconstruct Image data C11. For example, the processing function144 b may perform resampling of Image data C11 and then performsegmentation. For example, the processing function 144 b may performresampling of the segmented Image data C11 and then perform sequentialprojection. In the description with reference to FIG. 3, non-sparse fulldata is generated through sequential projection and then fabricated togenerate sparse data, but the processing function 144 b may generatesparse data through sequential projection.

As described above, the processing function 144 b generates Projectiondata set B14 by sequentially projecting Image data C11 and performsvarious kinds of fabrication on Projection data set B14. For example,the processing function 144 b equalizes the matrix dimensions ofProjection data set B14 and Projection data set B12 and fabricatesProjection data set B14 into a sparse state similar to that ofProjection data set B12. In other words, the processing function 144 bfabricates Projection data set B14 into a data format same as that ofProjection data set B12.

Subsequently, the processing function 144 b generates Projection dataset B16 by blending Projection data set B14 and Projection data set B12.For example, the processing function 144 b generates Projection data setB16 by positioning Projection data set B14 and Projection data set B12and performing weighted summation of Projection data set B14 andProjection data set B12 at a predetermined ratio. In other words, theprocessing function 144 b generates Projection data set B16 bycorrecting Projection data set B12 with Projection data set B14.

Alternatively, the processing function 144 b may perform correction ofProjection data set B12 based on Projection data set B14 by AI. Forexample, the processing function 144 b generates in advance a learnedmodel M1 functionalized to receive inputting of two projection data setsacquired from an identical target and output a high-quality projectiondata set, and stores the generated learned model M1 in the memory 141.Then, when Scanning A11 and Scanning A12 are executed, the processingfunction 144 b generates Projection data set B16 by inputting Projectiondata set B14 and Projection data set B12 to the learned model M1 readfrom the memory 141.

The following describes exemplary generation processing of the learnedmodel M1. First, the processing function 144 b acquires, as learningdata, a group of projection data sets acquired from an identical target.The group of Projection data set B21, Projection data set B22, andProjection data set B23 will be described below as exemplary learningdata. For example, Projection data set B21, Projection data set B22, andProjection data set B23 are acquired by performing three times ofscanning on the subject P1, a subject P2 different from the subject P1,a phantom as a replica of a human body, or the like. Projection data setB23 is high-quality projection data set acquired by using X-rays of ahigher radiation dose than that for Projection data set B21 andProjection data set B22. Projection data set B21, Projection data setB22, and Projection data set B23 may be acquired in the X-ray CT system10 or another system.

For example, the processing function 144 b generates the learned modelM1 by executing machine learning with Projection data set B21 andProjection data set B22 as input side data and with high-qualityProjection data set B23 as output side data.

The learned model M1 can be configured as, for example, a neuralnetwork. The neural network is a network having a structure in whichadjacent layers arranged in a layered structure are connected with eachother, and through which information propagates from an input layer sideto an output layer side. For example, the processing function 144 bgenerates the learned model M1 by executing deep learning on amulti-layer neural network by using the above-described learning data.The multi-layer neural network is constituted by, for example, an inputlayer, a plurality of intermediate layers (hidden layers), and an outputlayer.

For example, the processing function 144 b inputs Projection data setB21 and Projection data set B22 as input side data into a neuralnetwork. In the neural network, information propagates throughconnection of adjacent layers in one direction from the input layer sideto the output layer side, and a projection data set estimated asProjection data set B23 is output from the output layer.

For example, in the neural network, processing of positioning Projectiondata set B21 and Projection data set B22, determining weights ofProjection data set B21 and Projection data set B22, and blendingProjection data set B21 and Projection data set B22 is performed. Theweights of Projection data set B21 and Projection data set B22 may bedetermined for each pixel. Then, a projection data set estimated ashigh-quality Projection data set B23 is output from the output layer ofthe neural network. A neural network in which information propagates inone direction from the input layer side to the output layer side is alsocalled a convolutional neural network (CNN).

The processing function 144 b generates the learned model M1 byadjusting parameters of the neural network so that the neural networkcan output a preferable result when input side data is input. Forexample, the processing function 144 b adjusts parameters of the neuralnetwork by using a function (error function) indicating the distancebetween projection data sets.

For example, the processing function 144 b calculates an error functionindicating the distance between Projection data set B23 and a projectiondata set estimated by the neural network. Then, the processing function144 b adjusts parameters of the neural network so that the calculatederror function becomes at a local minimum. Accordingly, the processingfunction 144 b generates the learned model M1 functionalized to receiveinputting of two projection data sets acquired from an identical targetand output a high-quality projection data set. The processing function144 b stores the generated learned model M1 in the memory 141.

The learned model M1 may be functionalized to further performpreprocessing of blending of two input projection data sets. Forexample, when the matrices of the two input projection data sets aredifferent, the learned model M1 performs resampling of at least one ofthe two input projection data sets. For example, when one of the twoinput projection data sets is sparse data, the learned model M1fabricates the other projection data set into similar sparse data.

Although the learned model M1 is configured as a neural network in theabove description, the processing function 144 b may generate thelearned model M1 by a machine learning method other than a neuralnetwork. In addition, although the learned model M1 is generated by theprocessing function 144 b in the above description, the learned model M1may be generated by another apparatus.

After having generated Projection data set B16 based on Projection dataset B12 and Projection data set B14, the processing function 144 bexecutes material decomposition between two kinds of reference materialsbased on Projection data set B16 corresponding to Energy E12 andProjection data set B13 corresponding to Energy E13.

For example, the processing function 144 b first interpolates data ofany missing part of each projection data set. Specifically, sinceProjection data set B16 and Projection data set B13 are both sparsedata, the processing function 144 b produces full data by interpolatingdata of any missing part of each projection data set. Examples of theinterpolation processing by the processing function 144 b include linearinterpolation, Lagrange interpolation, and sigmoid. Then, the processingfunction 144 b executes material decomposition based on Projection dataset B16 and Projection data set B13 in the state of full data. Forexample, the processing function 144 b can separate a kidney stone froma soft tissue through material decomposition processing.

In addition, the processing function 144 b generates an imageillustrating a result of the material decomposition. For example, theprocessing function 144 b generates a material decomposition image inwhich “calcium” is emphasized and a material decomposition image inwhich “water” is emphasized. The processing function 144 b can alsogenerate various kinds of images at predetermined energy such as amonochromatic image, a density image, and an effective atomic numberimage. The control function 144 c causes the display 142 to display animage illustrating a result of the material decomposition.

In FIG. 3, the processing function 144 b may perform interpolationprocessing of any missing part of Projection data set B12 beforeblending of Projection data set B14 and Projection data set B12.Specifically, the processing function 144 b may omit processing offabricating Projection data set B14 into sparse data and blendProjection data set B14 and Projection data set B12 in the state of fulldata.

Although the matrix of projection data set acquired by Scanning A11 andthe matrix of the projection data set acquired by Scanning A12 aredifferent in the above description with reference to FIG. 3, thescanning function 144 a may execute Scanning A11 and Scanning A12 sothat the matrices of the projection data sets are same. In this case,the processing function 144 b may omit the processing of resampling.

When Energy E11 and Energy E12 are equal, the scanning function 144 amay omit the processing of reconstruction, segmentation, and sequentialprojection. Specifically, when Energy E11 and Energy E12 are equal, thescanning function 144 a may correct Projection data set B12 by directlyusing Projection data set B11 acquired by Scanning A11.

The following describes an exemplary procedure of processing at theX-ray CT system 10 with reference to FIG. 5. FIG. 5 is a flowchart fordescription of a series of processes of processing at the X-ray CTsystem 10 according to the first embodiment.

Steps S101 and S107 correspond to the scanning function 144 a. StepsS102, S103, S108, and S109 correspond to the processing function 144 b.Steps S104, S105, S106, and S110 correspond to the control function 144c.

First, the processing circuitry 144 executes Scanning A11 by irradiatingthe range R1 extending in the body axis direction of the subject P1 withX-rays and acquires Projection data set B11 corresponding to Energy E11(step S101). Subsequently, the processing circuitry 144 generates Imagedata C11 by executing reconstruction processing of Projection data setB11 (step S102).

Subsequently, the processing circuitry 144 generates Projection data setB14 corresponding to Energy E11 based on Projection data set B11 (stepS103). Specifically, the processing circuitry 144 generates Projectiondata set B14 by segmenting Image data C11 based on Projection data setB11 in accordance with the X-ray absorption coefficient and sequentiallyprojecting the segmented Image data C11 in accordance with Energy E12.

Then, the processing circuitry 144 generates a reference image byperforming rendering processing of Image data C11 (step S104) and causesthe display 142 to display the generated reference image (step S105).Then, the processing circuitry 144 determines whether the range R2 asthe scanning range of Scanning A12 is set by a user referring to thereference image (step S106). When the scanning range is yet to be set,the processing circuitry 144 becomes a standby state (No at step S106).

When the scanning range is set (Yes at step S106), the processingcircuitry 144 executes Scanning A12 in the scanning range set to thereference image (step S107). Specifically, the processing circuitry 144acquires Projection data set B12 corresponding to Energy E12 andProjection data set B13 corresponding to Energy E13 by irradiating therange R2 extending in the body axis direction of the subject P1 withX-rays.

Subsequently, the processing circuitry 144 generates Projection data setB16 corresponding to Energy E11 based on Projection data set B12 andProjection data set B14 (step S108). Subsequently, the processingcircuitry 144 performs material decomposition between two kinds ofreference materials based on Projection data set B16 corresponding toEnergy E11 and Projection data set B13 corresponding to Energy E13 (stepS109). Then, the processing circuitry 144 causes the display 142 todisplay a material decomposition image illustrating a result of thematerial decomposition (step S110), and ends the processing.

Step S103 and the set of steps S104, S105, S106, and S107 are performedin an optional order and may be performed in parallel.

Although X-ray energy is changed for each view in Scanning A12 in theabove description with reference to FIG. 3, the scanning function 144 amay change X-ray energy for each set of a plurality of views.

Although Scanning A12 is performed in dual energy (Energy E12 and EnergyE13) in the above description with reference to FIG. 3, the scanningfunction 144 a may execute, as Scanning A12, multi-energy scanning usingX-rays of three or more kinds of energy. In this case, the processingcircuitry 144 can perform material decomposition among three or morekinds of reference materials.

As described above, according to the first embodiment, the scanningfunction 144 a executes Scanning A11 of acquiring Projection data setB11 corresponding to Energy E11 by irradiating the range R1 extending inthe body axis direction of the subject P1 with X-rays. The scanningfunction 144 a also executes Scanning A12 of acquiring Projection dataset B12 corresponding to Energy E12 and Projection data set B13corresponding to Energy E13 by irradiating the range R2 extending in thebody axis direction of the subject P1 with X-rays. In addition, theprocessing function 144 b generates Projection data set B14corresponding to Energy E11 based on Projection data set B11. Theprocessing function 144 b also performs material decomposition among aplurality of reference materials based on Projection data set B14,Projection data set B12, and Projection data set B13. Thus, the X-ray CTsystem 10 according to the first embodiment can achieve improvedaccuracy of material decomposition.

In particular, the quality of Projection data set B12 as low-energy sidedata is often lower than the quality of Projection data set B13 ashigh-energy side data. The X-ray CT system 10 can improve the quality ofthe low-energy side data by correcting Projection data set B12 based onProjection data set B14. Accordingly, the X-ray CT system 10 can achieveimproved accuracy of material decomposition based on the low-energy sidedata and the high-energy side data.

The processing function 144 b generates Projection data set B14 bygenerating Image data C11 based on Projection data set B11, segmentingImage data C11 in accordance with the X-ray absorption coefficient,sequentially projecting the segmented Image data C11 in accordance withEnergy E12. At least part of noise contained in Projection data set B11is removed through the processing of reconstruction and sequentialprojection. Accordingly, Projection data set B14 becomes data havingnoise less than that of Projection data set B11. Thus, the X-ray CTsystem 10 can accurately correct Projection data set B12 by generatingProjection data set B14 as compared to a case in which Projection dataset B12 is corrected by directly using Projection data set B11.

Scanning A11 is positioning scanning for executing Scanning A12 andincluded in a typical processing process. Thus, execution of ScanningA11 does not complicate the processing process nor increase the amountof radiation exposure of the subject P1.

The X-ray CT system 10 uses a result of Scanning A11 in the positioningfor Scanning A12 and also in the processing of material decomposition.Thus, the X-ray CT system 10 can provide more meaningfulness toradiation exposure of the subject P1 through Scanning A11.

The first embodiment is described above, but various kinds of differentforms other than the above-described embodiment are possible.

For example, in the above-described embodiment, among Projection dataset B12 and Projection data set B13 acquired by Scanning A12, onlyProjection data set B12 is corrected based on Projection data set B11acquired by Scanning A11. However, the embodiment is not limitedthereto, and the processing function 144 b may correct Projection dataset B13 based on Projection data set B11.

For example, the processing function 144 b first generates Image dataC11 based on Projection data set B11 and segments Image data C11 inaccordance with the X-ray absorption coefficient. Subsequently, theprocessing function 144 b generates Projection data set B15 bysequentially projecting the segmented Image data C11 in accordance withEnergy E13. Projection data set B15 is an exemplary fifth projectiondata set.

Subsequently, the processing function 144 b generates Projection dataset B17 corresponding to Energy E13 based on Projection data set B13 andProjection data set B15. Specifically, the processing function 144 bgenerates Projection data set B17 by correcting Projection data set B13based on Projection data set B15. For example, the processing function144 b generates Projection data set B17 by inputting Projection data setB13 and Projection data set B15 to the learned model M1. Projection dataset B17 is an exemplary seventh projection data set or fourth subjectdata set. Then, the processing function 144 b performs materialdecomposition based on Projection data set B12 corresponding to EnergyE12 and Projection data set B17 corresponding to Energy E13.

The quality of Projection data set B13 as high-energy side data istypically high, but when the physical size of the subject P1 is large,noise is contained in Projection data set B13 in some cases. Inaddition, artifacts occur to Projection data set B13 in some cases dueto various kinds of factors such as an electrical discharging phenomenonthat occurs in the X-ray tube 111 and body motion of the subject P1. Insuch a case, the processing function 144 b can correct Projection dataset B13 based on Projection data set B11 acquired by Scanning A11.

Alternatively, the processing function 144 b may correct both Projectiondata set B12 and Projection data set B13 based on Projection data setB11 acquired by Scanning A11.

For example, the processing function 144 b first generates Image dataC11 based on Projection data set B11 and segments Image data C11 inaccordance with the X-ray absorption coefficient. Subsequently, theprocessing function 144 b generates Projection data set B14 bysequentially projecting the segmented Image data C11 in accordance withEnergy E12. The processing function 144 b also generates Projection dataset B15 by sequentially projecting the segmented Image data C11 inaccordance with Energy E13.

Subsequently, the processing function 144 b generates Projection dataset B16 corresponding to Energy E12 based on Projection data set B12 andProjection data set B14. For example, the processing function 144 bgenerates Projection data set B16 by inputting Projection data set B12and Projection data set B14 into the learned model M1. The processingfunction 144 b also generates Projection data set B17 corresponding toEnergy E13 based on Projection data set B13 and Projection data set B15.For example, the processing function 144 b generates Projection data setB17 by inputting Projection data set B13 and Projection data set B15into the learned model M1. Then, the processing function 144 b performsmaterial decomposition based on Projection data set B16 corresponding toEnergy E11 and Projection data set B17 corresponding to Energy E13.

In the above-described embodiment, Projection data set B14 andProjection data set B15 are generated from Projection data set B11 bythe processing of reconstruction, segmentation, and sequentialprojection. However, the embodiment is not limited thereto.

For example, the processing function 144 b may generate Projection dataset B14 and Projection data set B15 from Projection data set B11 byscaling processing. The scaling processing generates data of differentenergies based on a property that the transmission amount of an X-raychanges in accordance with the energy of the X-ray. For example, theprocessing function 144 b generates Projection data set B14corresponding to Energy E12 by approximating the transmission amount ofan X-ray in Projection data set B11 to the transmission amount of theX-ray when the energy of the X-ray is Energy E12 through multiplicationof Projection data set B11 by a coefficient in accordance with thedifference between Energy E11 and Energy E12. For example, theprocessing function 144 b also generates Projection data set B15corresponding to Energy E13 by approximating the transmission amount ofan X-ray in Projection data set B11 to the transmission amount of theX-ray when the energy of the X-ray is Energy E13 through multiplicationof Projection data set B11 by a coefficient in accordance with thedifference between Energy E11 and Energy E13.

In the above-described embodiment, the range R2 as the scanning range ofScanning A12 is set by a user, but the embodiment is not limitedthereto. For example, the control function 144 c may automatically setthe range R2 by analyzing Image data C11 or a reference image generatedbased on Image data C11 and extracting an organ or the like as adiagnosis target.

In the above-described embodiment, Scanning A11 is positioning scanningfor setting the range R2 as the scanning range of Scanning A12, but theembodiment is not limited thereto. For example, Scanning A11 may bescanning executed on a day different from a day on which Scanning A12 isexecuted. Scanning A11 may be scanning executed after Scanning A12.

In the above-described embodiment, Scanning A12 of the kV switchingscheme is executed to acquire Projection data set B12 and Projectiondata set B13. However, the embodiment is not limited thereto.

For example, the scanning function 144 a may execute, in place ofScanning A12, Scanning A13 of a layered detector scheme (also referredto as a dual layer scheme). In this case, the X-ray CT system 10includes a layered detector as the X-ray detector 112. For example, theX-ray detector 112 includes a first layer 112 a and a second layer 112 band disperses and detects an X-ray emitted from the X-ray tube 111.Then, the scanning function 144 a can acquire, by executing ScanningA13, Projection data set B12 based on a result of the detection by thefirst layer 112 a and Projection data set B13 based on a result of thedetection by the second layer 112 b.

Alternatively, for example, the scanning function 144 a may executeScanning A14 of a dual source scheme in place of Scanning A12 orScanning A13. In this case, the X-ray CT system 10 includes an X-raytube 1111 and an X-ray tube 1112 as the X-ray tube 111. The X-ray CTsystem 10 also includes, as the X-ray detector 112, an X-ray detector1121 configured to detect an X-ray emitted from the X-ray tube 1111, andan X-ray detector 1122 configured to detect an X-ray emitted from theX-ray tube 1112. Then, the scanning function 144 a can acquire, byexecuting Scanning A14, Projection data set B12 based on a result of thedetection by the X-ray detector 1121 and Projection data set B13 basedon a result of the detection by the X-ray detector 1122.

Alternatively, for example, the scanning function 144 a may executeScanning A15 of a split scheme in place of Scanning A12, Scanning A13,or Scanning A14. In this case, the X-ray CT system 10 includes, as thewedge 116, a filter configured to divide an X-ray emitted from the X-raytube 111 into a plurality of X-rays having different energies.

For example, the X-ray CT system 10 includes, as the wedge 116, an X-rayfilter 116 a and an X-ray filter 116 b. The X-ray filter 116 a and theX-ray filter 116 b have different materials, thicknesses, and the likeand divide an X-ray having an identical energy into a plurality ofX-rays having different energies. In this case, the scanning function144 a attenuates the energy of an X-ray emitted from the X-ray tube 111to Energy E12 through the X-ray filter 116 a and to Energy E13 throughthe X-ray filter 116 b and executes Scanning A15. More specifically, ina configuration that the X-ray filter 116 a and the X-ray filter 116 bare arranged in the Z-axis direction (column direction) illustrated inFIG. 1, the scanning function 144 a irradiates the X-ray filter 116 aand the X-ray filter 116 b with X-rays from the X-ray tube 111.Accordingly, the scanning function 144 a can acquire Projection data setB12 corresponding to Energy E12 and Projection data set B13corresponding to Energy E13 by executing Scanning A15 while X-rays ofEnergy E12 and X-rays of Energy E13 are distributed in the columndirection.

For example, the X-ray CT system 10 includes only an X-ray filter 116 cas the wedge 116. For example, the scanning function 144 a emits X-raysof Energy E13 from the X-ray tube 111, attenuates some X-rays to EnergyE12 through the X-ray filter 116 c, and executes Scanning A15. Morespecifically, the scanning function 144 a attenuates X-rays in apredetermined range in the column direction from Energy E13 to EnergyE12 through the X-ray filter 116 b and irradiates the subject P1 withthe X-rays. X-rays not subjected to the attenuation through the X-rayfilter 116 b are incident on the subject P1 at Energy E13. Accordingly,the scanning function 144 a can acquire Projection data set B12corresponding to Energy E12 and Projection data set B13 corresponding toEnergy E13 by executing Scanning A15 while X-rays of Energy E11 andX-rays of Energy E13 are distributed in the column direction.

Projection data set B12 and Projection data set B13 acquired by the duallayer scheme, the dual source scheme, or the split scheme are not sparsedata, and thus the processing function 144 b does not need performinterpolation processing on Projection data set B12 and Projection dataset B13 nor fabrication of Projection data set B14 and Projection dataset B15 based on Projection data set B11 into sparse data.

In the above-described embodiment, Projection data set B12 andProjection data set B13 are correction targets. Specifically, correctionis performed in a projection data region in the above-describedembodiment. However, the embodiment is not limited thereto. For example,the processing function 144 b may perform correction in an image region.

For example, the processing function 144 b reconstructsthree-dimensional Image data C11 corresponding to Energy E11 based onProjection data set B11 acquired by Scanning A11. The processingfunction 144 b also reconstructs three-dimensional Image data C12corresponding to Energy E12 based on Projection data set B12 acquired byScanning A12. The processing function 144 b also reconstructsthree-dimensional Image data C13 corresponding to Energy E13 based onProjection data set B13 acquired by Scanning A12. Image data C11 is anexemplary first subject data set. Image data C12 is an exemplary secondsubject data set. Image data C13 is an exemplary third subject data set.

Subsequently, the processing function 144 b corrects at least one ofImage data C12 and Image data C13 based on Image data C11. Image dataC11 corresponds to an X-ray energy different from those of Image dataC12 and Image data C13 but has valuable information on X-ray attenuationand an anatomical structure in the subject P1. Thus, the processingfunction 144 b can improve the quality of reconstruction images such asImage data C12 and Image data C13 based on Image data C11. Then, theprocessing function 144 b executes the processing of materialdecomposition based on Image data C12 and Image data C13, at least oneof which is corrected.

When performing correction in an image region, the processing function144 b may omit the above-described processing of generating Projectiondata set B14 and Projection data set B15. Specifically, the processingfunction 144 b may omit processing of simulating a projection data setof other energy.

The above-described correction processing in an image region can also beachieved by a machine learning method. For example, the processingfunction 144 b generates in advance a learned model M2 functionalized toreceive inputting of image data based on the first scanning and imagedata based on the second scanning and correct the image data based onthe second scanning, and stores the learned model M2 in the memory 141.When Scanning A11 and Scanning A12 are executed, the processing function144 b generates Image data C12′ corrected from Image data C12 byinputting Image data C11 based on Scanning A11 and Image data C12 basedon Scanning A12 to the learned model M2 read from the memory 141. Then,the processing function 144 b executes the processing of materialdecomposition based on Image data C12′ corresponding to Energy E12 andImage data C13 corresponding to Energy E13. Image data C12′ is anexemplary fourth subject data set. Specifically, the processing function144 b executes the processing of material decomposition based on: thefourth subject data set obtained based on the first subject data set andthe second subject data set; and the third subject data set.

Alternatively, the processing function 144 b generates Image data C13′corrected from Image data C13 by inputting Image data C11 based onScanning A11 and Image data C13 based on Scanning A12 to the learnedmodel M2. Then, the processing function 144 b executes the processing ofmaterial decomposition based on Image data C12 corresponding to EnergyE11 and Image data C13 corresponding to Energy E13′. Image data C13′ isan exemplary fourth subject data set. Specifically, the processingfunction 144 b executes the processing of material decomposition basedon: the fourth subject data set obtained based on the first subject dataset and the third subject data set; and the second subject data set.

Alternatively, the processing function 144 b generates, by using thelearned model M2, both Image data C12′ corrected from Image data C12 andImage data C13′ corrected from Image data C13. Then, the processingfunction 144 b executes the processing of material decomposition basedon Image data C12 corresponding to Energy E12′ and Image data C13corresponding to Energy E13′. Specifically, the processing function 144b executes the processing of material decomposition based on: the fourthsubject data set obtained based on the first subject data set and thesecond subject data set; and the fourth subject data set obtained basedon the first subject data set and the third subject data set.

The learned model M2 may be configured in a manner similar to thelearned model M1. For example, the learned model M2 may be configured asa neural network. For example, the processing function 144 b generatesthe learned model M2 by executing deep learning on a multi-layer neuralnetwork. The type of a neural network is not particularly limited butmay be a convolution neural network (CNN) or any other neural networksuch as a fully connected neural network or a recurrent neural network(RNN). Alternatively, the processing function 144 b may generate thelearned model M2 by a machine learning method other than a method usinga neural network. The processing function 144 b does not need to performgeneration processing of the learned model M2 but may acquire and usethe learned model M2 generated by another apparatus.

In the above description, at least Projection data set B12 correspondingto Energy E11 and Projection data set B13 corresponding to Energy E13are acquired in the second scanning. However, the embodiment is notlimited thereto. For example, the scanning function 144 a may acquireany one of Projection data set B12 and Projection data set B13 in thesecond scanning. In this case, the processing function 144 b can correctany one of Projection data set B12 and Projection data set B13 based onProjection data set B11 and reconstruct high-quality CT image data basedon the corrected projection data set. Alternatively, the processingfunction 144 b may correct, based on Image data C11 reconstructed fromProjection data set B11, any one of Image data C12 reconstructed fromProjection data set B12 and Image data C13 reconstructed from Projectiondata set B13, thereby improving the quality.

In the above description, material decomposition is executed by theX-ray CT system 10, but the embodiment is not limited thereto. Forexample, material decomposition may be executed by another apparatusdifferent from the X-ray CT system 10. This point will be describedbelow with an example of a medical information processing system 1illustrated in FIG. 6. FIG. 6 is a block diagram illustrating anexemplary configuration of the medical information processing system 1according to a second embodiment. The medical information processingsystem 1 includes the X-ray CT system 10 and a medical processingapparatus 20 configured to execute material decomposition.

As illustrated in FIG. 6, the X-ray CT system 10 and the medicalprocessing apparatus 20 are connected with each other through a networkNW. The X-ray CT system 10 and the medical processing apparatus 20 maybe installed at optional places as long as connection therebetween ispossible through the network NW. For example, the medical processingapparatus 20 may be installed at a hospital different from the place ofthe X-ray CT system 10. Specifically, the network NW may be configuredas a local network closed to the hospital or a network through theInternet. Although one X-ray CT system 10 is illustrated in FIG. 6, aplurality of X-ray CT systems 10 may be included in the medicalinformation processing system 1.

The medical processing apparatus 20 is achieved by a computer such as aworkstation. For example, as illustrated in FIG. 6, the medicalprocessing apparatus 20 includes a memory 21, a display 22, an inputinterface 23, and processing circuitry 24.

The memory 21 is achieved by, for example, a semiconductor memoryelement such as a RAM or a flash memory, a hard disk, or an opticaldisk. For example, the memory 21 stores various kinds of datatransmitted from the X-ray CT system 10. For example, the memory 21 alsostores a computer program for circuitry included in the medicalprocessing apparatus 20 to achieve its function. The memory 21 may beachieved by servers (cloud) connected with the medical processingapparatus 20 through the network NW.

The display 22 displays various kinds of information. For example, thedisplay 22 displays an image illustrating a result of materialdecomposition by the processing circuitry 24, a GUI for receivingvarious operations from a user, and the like. For example, the display22 is a liquid crystal display or a CRT display. The display 22 may be adesktop display, a tablet terminal capable of performing wirelesscommunication with the medical processing apparatus 20, or the like.

The input interface 23 receives various input operations from the user,converts such a received input operation into an electric signal, andoutputs the electric signal to the processing circuitry 24. For example,the input interface 23 receives a reconstruction condition onreconstruction of CT image data, an image processing condition ongeneration of a display CT image from CT image data, and the like fromthe user. For example, the input interface 23 is achieved by a mouse, akeyboard, a truck ball, a switch, a button, a joystick, a touch pad onwhich an input operation is performed through touch on an operationsurface, a touch screen as integration of a display screen and a touchpad, non-contact input circuitry using an optical sensor, voice inputcircuitry, or the like. The input interface 23 may be achieved by atablet terminal or the like capable of performing wireless communicationwith the medical processing apparatus 20. The input interface 23 is notlimited to a configuration including a physical operation component suchas a mouse or a keyboard. Examples of the input interface 23 includedelectric signal processing circuitry that receives an electric signalcorresponding to an input operation from an external input instrumentprovided separately from the medical processing apparatus 20 and outputsthe electric signal to the processing circuitry 24.

The processing circuitry 24 controls operation of the entire medicalprocessing apparatus 20 by executing a processing function 24 a and acontrol function 24 b. The processing function 24 a is an exemplaryprocessing unit. Processing by the processing circuitry 24 will bedescribed later.

In the medical processing apparatus 20 illustrated in FIG. 6, eachprocessing function is stored in the memory 21 in the form of acomputer-executable program. The processing circuitry 24 is a processorconfigured to read a computer program from the memory 21 and execute thecomputer program to achieve a function corresponding to the computerprogram. In other words, the processing circuitry 24 having read acomputer program has a function corresponding to the read computerprogram.

The processing function 24 a and the control function 24 b are achievedby the single processing circuitry 24 in the above description withreference to FIG. 6, but the processing circuitry 24 may be configuredas a combination of a plurality of independent processors, and eachprocessor may execute a computer program to achieve the correspondingfunction. The processing functions of the processing circuitry 24 may bedistributed or integrated to one or a plurality of processing circuitsas appropriate.

Alternatively, the processing circuitry 24 may achieve a function byusing a processor of an external apparatus connected through the networkNW. For example, the processing circuitry 24 achieves each functionillustrated in FIG. 6 by reading and executing a computer programcorresponding to each function from the memory 21 and by using, ascalculation resources, servers (cloud) connected with the medicalprocessing apparatus 20 through the network NW.

For example, the scanning function 144 a in the X-ray CT system 10 firstexecutes Scanning A11 by irradiating the range R1 extending in the bodyaxis direction of the subject P1 with X-rays and acquires Projectiondata set B11 corresponding to Energy E11. The scanning function 144 aalso executes Scanning A12 by irradiating the range R2 extending in thebody axis direction of the subject P1 with X-rays and acquiresProjection data set B12 corresponding to Energy E12 and Projection dataset B13 corresponding to Energy E13. The scanning function 144 a mayexecute, in place of Scanning A12 of the kV switching scheme, ScanningA13 of the dual layer scheme, Scanning A14 of the dual source scheme, orScanning A15 of the split scheme. The control function 144 c transmitsProjection data set B11, Projection data set B12, and Projection dataset B13 to the medical processing apparatus 20 through the network NW.

Subsequently, the processing function 24 a of the medical processingapparatus 20 generates at least one of Projection data set B14corresponding to Energy E12 and Projection data set B15 corresponding toEnergy E13 based on Projection data set B11. The processing function 24a performs material decomposition based on at least one of Projectiondata set B14 and Projection data set B15, Projection data set B12, andProjection data set B13.

For example, the processing function 24 a generates Projection data setB16 corresponding to Energy E11 based on Projection data set B12 andProjection data set B14 and performs material decomposition based onProjection data set B16 and Projection data set B13. For example, theprocessing function 24 a also generates Projection data set B17corresponding to Energy E13 based on Projection data set B13 andProjection data set B15 and performs material decomposition based onProjection data set B12 and Projection data set B17. For example, theprocessing function 24 a also generates Projection data set B16 based onProjection data set B12 and Projection data set B14, generatesProjection data set B17 based on Projection data set B13 and Projectiondata set B15, and performs material decomposition based on Projectiondata set B16 and Projection data set B17.

The control function 24 b causes the display 22 to display an imageillustrating a result of the material decomposition by the processingfunction 24 a. Alternatively, the control function 24 b transmits theimage illustrating a result of the material decomposition to the X-rayCT system 10. In this case, the control function 144 c in the X-ray CTsystem 10 causes the display 142 to display the image illustrating aresult of the material decomposition.

The term “processor” used in the above description means, for example, aCPU, a graphics processing unit (GPU), or circuitry such as anapplication specific integrated circuit (ASIC) or a programmable logicdevice (for example, a simple programmable logic device (SPLD), acomplex programmable logic device (CPLD), or a field programmable gatearray (FPGA)). The processor achieves a function by reading andexecuting a computer program stored in the memory 141 or the memory 21.

In the above description with reference to FIG. 1, the single memory 141stores a computer program corresponding to each processing function ofthe processing circuitry 144. In the above description with reference toFIG. 6, the single memory 21 stores a computer program corresponding toeach processing function of the processing circuitry 24. However, theembodiment is not limited thereto. For example, a plurality of memories141 may be disposed in a distributed manner, and the processingcircuitry 144 may read a computer program from the corresponding memory141. Similarly, a plurality of memories 21 may be disposed in adistributed manner, and the processing circuitry 24 may read a computerprogram from the corresponding memory 21. Instead of being stored in thememory 141 or the memory 21, a computer program may be directlyincorporated in a circuit of a processor. In this case, the processorachieves a function by reading and executing the computer programincorporated in the circuit.

Each component of each apparatus according to the above-describedembodiments is functionally conceptual and does not necessarily need tobe physically configured as illustrated in the drawings. In other words,the specific form of distribution and integration of the apparatus isnot limited to those illustrated in the drawings, but the entire or partthereof may be functionally or physically distributed and integrated inarbitrary units in accordance with, for example, various loads and usestatuses. Moreover, the entire or an optional part of each processingfunction performed at each apparatus

may be achieved by a CPU and a computer program analyzed and executed bythe CPU or may be achieved as wired logic hardware.

Each processing method described in the above-described embodiments canbe achieved by executing a processing computer program prepared inadvance through a computer such as a personal computer or a workstation.The processing computer program may be distributed through a networksuch as the Internet. In addition, the processing computer program maybe recorded in a computer-readable non-transient recording medium suchas a hard disk, a flexible disk (FD), a CD-ROM, an MO, or a DVD, readfrom the recording medium by a computer, and executed.

According to at least one embodiment described above, it is possible toimprove the accuracy of material decomposition.

While certain embodiments have been described, these embodiments havebeen presented by way of example only, and are not intended to limit thescope of the inventions. Indeed, the novel embodiments described hereinmay be embodied in a variety of other forms; furthermore, variousomissions, substitutions and changes in the form of the embodimentsdescribed herein may be made without departing from the spirit of theinventions. The accompanying claims and their equivalents are intendedto cover such forms or modifications as would fall within the scope andspirit of the inventions.

What is claimed is:
 1. An X-ray CT system comprising processingcircuitry configured to: execute first scanning of acquiring a firstsubject data set corresponding to first X-ray energy by irradiating afirst region of a subject with X-rays; execute, after the firstscanning, second scanning of acquiring a second subject data setcorresponding to second X-ray energy and a third subject data setcorresponding to third X-ray energy different from the second X-rayenergy by irradiating a second region included in the first region withX-rays; and perform material decomposition among a plurality ofreference materials based on: a fourth subject data set obtained basedon the first subject data set and one of the second subject data set andthe third subject data set; and the other of the second subject data setand the third subject data set.
 2. The X-ray CT system according toclaim 1, wherein the processing circuitry acquires a first projectiondata set as the first subject data set through execution of the firstscanning by irradiating the first region extending in a body axisdirection of the subject with X-rays, acquires a second projection dataset as the second subject data set and acquires a third projection dataset as the third subject data set through execution of, after the firstscanning, the second scanning by irradiating a second region narrowerthan the first region extending in the body axis direction with X-rays,generates, based on the first projection data set, at least one of afourth projection data set corresponding to the second X-ray energy anda fifth projection data set corresponding to the third X-ray energy, andperforms the material decomposition based on: the fourth projection dataset and the second projection data set; the fourth subject data setobtained based on one of the fifth projection data set and the thirdprojection data set; and the other of the second projection data set andthe third projection data set.
 3. The X-ray CT system according to claim1, wherein the first X-ray energy is different from the second X-rayenergy and the third X-ray energy.
 4. The X-ray CT system according toclaim 2, wherein the second X-ray energy is smaller than the third X-rayenergy, and the processing circuitry generates the fourth projectiondata set corresponding to the second X-ray energy based on the firstprojection data set, generates a sixth projection data set correspondingto the second X-ray energy based on the second projection data set andthe fourth projection data set, and performs the material decompositionbased on the third projection data set and the sixth projection dataset.
 5. The X-ray CT system according to claim 2, wherein the processingcircuitry generates, based on the first projection data set, the fourthprojection data set corresponding to the second X-ray energy and thefifth projection data set corresponding to the third X-ray energy,generates, based on the second projection data set and the fourthprojection data set, a sixth projection data set corresponding to thesecond X-ray energy, generates, based on the third projection data setand the fifth projection data set, a seventh projection data setcorresponding to the third X-ray energy, and performs the materialdecomposition based on the sixth projection data set and the seventhprojection data set.
 6. The X-ray CT system according to claim 4,wherein the processing circuitry generates the fourth projection dataset by generating three-dimensional first image data based on the firstprojection data set, segmenting the first image data in accordance withan X-ray absorption coefficient, and sequentially projecting thesegmented first image data in accordance with the second X-ray energy.7. The X-ray CT system according to claim 5, wherein the processingcircuitry generates the fourth projection data set by generatingthree-dimensional first image data based on the first projection dataset, segmenting the first image data in accordance with an X-rayabsorption coefficient, and sequentially projecting the segmented firstimage data in accordance with the second X-ray energy, and generates thefifth projection data set by sequentially projecting the segmented firstimage data in accordance with the third X-ray energy.
 8. The X-ray CTsystem according to claim 4, wherein the processing circuitry generatesthe sixth projection data set by inputting the fourth projection dataset and the second projection data set to a learned model functionalizedto receive inputting of two projection data sets acquired from anidentical target and output a high-quality projection data set.
 9. TheX-ray CT system according to claim 5, wherein the processing circuitrygenerates the sixth projection data set by inputting the fourthprojection data set and the second projection data set to a learnedmodel functionalized to receive inputting of two projection data setsacquired from an identical target and output a high-quality projectiondata set, and generates the seventh projection data set by inputting thefifth projection data set and the third projection data set to thelearned model.
 10. The X-ray CT system according to claim 1, wherein theprocessing circuitry executes the second scanning by changing X-rayenergy between the second X-ray energy and the third X-ray energy foreach of one or a plurality of views.
 11. The X-ray CT system accordingto claim 1, wherein the processing circuitry executes the secondscanning by emitting X-rays of the second X-ray energy through a firstX-ray tube and emitting X-rays of the third X-ray energy through asecond X-ray tube.
 12. The X-ray CT system according to claim 1, whereinthe processing circuitry executes the second scanning by using a layeredX-ray detector including a first layer that detects X-rays of the secondX-ray energy and a second layer that detects X-rays of the third X-rayenergy.
 13. The X-ray CT system according to claim 1, wherein theprocessing circuitry executes the second scanning by using a first X-rayfilter that attenuates an X-ray transmitting through the first X-rayfilter to the second X-ray energy and a second X-ray filter thatattenuates an X-ray transmitting through the second X-ray filter to thethird X-ray energy.
 14. The X-ray CT system according to claim 1,wherein the processing circuitry executes the second scanning byattenuating part of X-rays incident on the subject by using an X-rayfilter that attenuates an X-ray transmitting through the X-ray filterfrom the second X-ray energy to the third X-ray energy.
 15. A medicalprocessing apparatus comprising processing circuitry configured toperform material decomposition among a plurality of reference materialsbased on: a fourth subject data set obtained based on a first subjectdata set corresponding to first X-ray energy and acquired by irradiatinga first region of a subject with X-rays in first scanning and one of asecond subject data set corresponding to second X-ray energy and a thirdsubject data set corresponding to third X-ray energy different from thesecond X-ray energy, the second subject data set and the third subjectdata set being acquired by irradiating a second region included in thefirst region with X-rays in second scanning executed after the firstscanning; and the other of the second subject data set and the thirdsubject data set.